FEA Implement categorical feature support to IterativeImputer
#31479
+561
−34
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…e support Our implementation automatically detects categorical columns (based on dtype), uses a RandomForestClassifier to model and predict missing category labels, and then inverse-transforms those predictions back into the original categories. So, if given a certain X data, where X features are numerical and categorical, respectively, and there are values missing (nan) in the categorical one, _iterative.py, through the Random Forest Classifier, will predict those missing values and replace them in the data. Co-authored-by: Fabioprata23 <fabio.prata@tecnico.ulisboa.pt>
IterativeImputer
|
@betatim Hi. Could you help me out here please? |
|
The CI tests many different versions and optional dependencies that you might not have locally. That is probably why it doesn't fail locally. The log output for the failures is often helpful for understanding why something breaks. Or you can see what versions of packages are used in the CI job that fails and see if that reproduces the errors locally. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Fixes #31219
Our implementation automatically detects categorical columns
(based on dtype), uses a RandomForestClassifier to model and predict missing category labels, and then inverse-transforms those predictions back into the original categories. So, if given a certain X data, where X features are numerical and categorical, respectively, and there are values missing (nan) in the categorical one, _iterative.py, through the Random Forest Classifier, will predict those missing values and replace them in the data.
Co-authored-by: Fabioprata23 fabio.prata@tecnico.ulisboa.pt
Reference Issues/PRs
What does this implement/fix? Explain your changes.
Any other comments?